Method and system for determining correlated geographic areas

ABSTRACT

A method of determining a geographic area having similar characteristics to a first geographic area associated with a user includes receiving a plurality of inputs related to characteristics of a plurality of geographic areas and constructing a feature vector for each of the geographic areas based on the plurality of inputs. The method also includes receiving a plurality of inputs related to characteristics of the first geographic area and constructing a feature vector for the first geographic area associated with the user. The method further includes receiving an input from the user related to a city of interest, comparing the feature vector for the first geographic area to feature vectors associated with geographic areas located in or adjacent to the city of interest, and ranking the geographic areas located in or adjacent to the city of interest using the comparing step.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/215,022, filed on Aug. 22, 2011 and issued as U.S. Pat. No.8,732,219, which claims priority to U.S. Provisional Patent ApplicationNo. 61/376,975, filed on Aug. 25, 2010, entitled “Method and System forDetermining Correlated Geographic Areas.” The disclosures of theseapplications are hereby incorporated by reference in their entirety forall purposes.

BACKGROUND OF THE INVENTION

Customers demand more of the products and services they use than everbefore. They insist that the companies they deal with on a regular basisprovide them greater and greater levels of accuracy and more tailoredservice offerings. Companies configure and operate ever increasingnumbers of computer systems to achieve this. Using sources ofinformation that have traditionally been unavailable when servicingthese customers is now expected.

SUMMARY OF THE INVENTION

The present invention relates generally to real estate systems. Morespecifically, the present invention relates to methods and systems forproviding information related to correlations between neighborhoods.Merely by way of example, the invention has been applied to a method ofproviding a user with information on neighborhoods comparable to theuser's current, past, or desired neighborhood. The methods andtechniques can be applied to a variety of real estate, insurance, andfinancing systems.

According to an embodiment of the present invention, a method providesdetermining a geographic area having similar characteristics to a firstgeographic area associated with a user. The method includes receiving aplurality of inputs related to characteristics of a plurality ofgeographic areas, and constructing a feature vector for each of theplurality of geographic areas based on the plurality of inputs. Themethod also includes receiving a plurality of inputs related tocharacteristics of the first geographic area and constructing a featurevector for the first geographic area. The method further includesreceiving an input from the user related to a city of interest,comparing the feature vector for the first geographic area to featurevectors associated with geographic areas located in or adjacent to thecity of interest, and ranking the geographic areas located in oradjacent to the city of interest using the comparing step.

According to another embodiment, a method of suggesting a newneighborhood to a user based on a current neighborhood is provided. Themethod includes computing a current feature vector for the currentneighborhood, wherein the current feature vector is based on a pluralityof characteristics of the current neighborhood. The method also includesreceiving an input from the user related to a city of interest andretrieving a plurality of feature vectors from a database storingfeature vectors associated with neighborhoods in the city of interest.The method further includes comparing the current feature vector witheach of the plurality of feature vectors and determining a neighborhoodin the city of interest defined by a minimum distance between thecurrent feature vector and one of the plurality of feature vectors. Insome embodiments, a method further includes ranking the neighborhoods inthe city of interest by increasing distance between the feature vectorfor the current neighborhood and the plurality of feature vectors.

According to yet another embodiment, a method provides comparing acurrent neighborhood to a desired neighborhood in a city of interestselected by a user. The method includes computing a current featurevector based on characteristics associated with the currentneighborhood. The method also includes receiving the selection of thecity of interest from the user, and accessing a plurality of featurevectors from a database, wherein each of the plurality of featurevectors is associated with one of the plurality of neighborhoods in thecity of interest. The method further includes determining a plurality ofneighborhoods in the city of interest characterized by a minimumdifference between the current feature vector and each of the pluralityof feature vectors. In addition, the method may include displaying theplurality of neighborhoods to the user and receiving a selection of adesired neighborhood from the user. After receiving the selection, themethod includes computing a difference between the current featurevector and the feature vector associated with the desired neighborhoodand displaying the difference to the user. In some embodiments,displaying the difference comprises displaying a cost differentialbetween a similar home in the current neighborhood and the desiredneighborhood. In other embodiments, the characteristics associated withthe current neighborhood include characteristics associated with theuser.

According to yet another embodiment, a method provides a user with aneighborhood ranking. The method includes receiving an identification ofa city including a plurality of neighborhoods, receiving a listing ofhomes located in the city, and parsing the listings into subsets oflistings, each subset associated with one neighborhood. The method alsoincludes computing, using a processor, a feature vector for the homes ineach subset of listings, and for each neighborhood, forming aneighborhood feature vector by combining the feature vectors for thehomes in each subset of listings. The method further includes computinga current feature vector associated with a current home of the user,comparing the current feature vector with the neighborhood featurevectors for each neighborhood, ranking the neighborhoods using thecomparison, and providing the user with the ranking.

In embodiments described above, an indication of the city may bereceived as an input from the user through a graphical user interface.In a particular embodiment, the current feature vector is a function ofattributes associated with the current home. In another particularembodiment, the method may further include adjusting the current featurevector using user input prior to comparing the current feature vectorwith the neighborhood feature vectors for each neighborhood. Also, thecurrent feature vector may include an N-dimensional vector based on Nattributes associated with the current home.

According to yet another embodiment, a method provides suggesting a newneighborhood to a user using information related to the user's previousneighborhoods. The method includes computing a timeline feature vectorfor each of the user's previous neighborhoods, each of the user'sprevious neighborhoods having a plurality of characteristics associatedtherewith, wherein the timeline feature vector is calculated using theplurality of characteristics associated with each of the user's previousneighborhoods. The method also includes determining a combinationtimeline feature vector using one or more of the timeline featurevectors, wherein the combination timeline feature vector is predictiveof the user's preference for the new neighborhood using the informationrelated to the user's previous neighborhoods. The method furtherincludes receiving an input from the user related to a city of interest,retrieving a plurality of feature vectors from a database storingfeature vectors associated with neighborhoods located in or adjacent tothe city of interest, and comparing the combination timeline featurevector with each of the plurality of feature vectors. Additionally, themethod includes determining one or more of the neighborhoods located inor adjacent to the city of interest defined by a minimum distancebetween the combination timeline feature vector and one of the pluralityof feature vectors.

According to yet another embodiment, a method provides suggesting a newneighborhood to a user. The method includes computing a current featurevector for a current neighborhood in or adjacent to a current city,wherein the current feature vector is calculated using a plurality ofcharacteristics of the current neighborhood and wherein the currentneighborhood has a selected characteristic and the current city has acorresponding selected characteristic. The method also includescalculating a first percent difference between a mean value of theselected characteristic associated the current neighborhood and a meanvalue of the corresponding selected characteristic associated with thecurrent city. The method further includes receiving an input from theuser related to a city of interest, retrieving a plurality of featurevectors from a database storing feature vectors associated withneighborhoods located in or adjacent to the city of interest, whereineach of the neighborhoods has the selected characteristic and the cityof interest has the corresponding selected characteristic. Then thecurrent feature vector is compared with each of the plurality of featurevectors. Also, the method includes calculating a second percentdifference between a mean value of the selected characteristicassociated each of the neighborhoods located in or adjacent to the cityof interest and a mean value of the corresponding selectedcharacteristic associated with the city of interest. The method thendetermines one or more of the neighborhoods located in or adjacent tothe city of interest characterized by: a minimum difference between thecurrent feature vector and each of the plurality of feature vectors; andthe first percent difference being similar to the second percentdifference.

Numerous benefits are achieved by way of the present invention overconventional techniques. For example, embodiments of the presentinvention provide methods and systems for suggesting new neighborhoodsto a user based on a variety of inputs including a current neighborhood,a listing of desired neighborhood attributes or characteristics,characteristics associated with a user's current home, a listing ofdesired home attributes or characteristics, combinations thereof, or thelike. As described more fully throughout the present specification,embodiments of the present invention utilize feature vectors tocharacterize homes, neighborhoods, geographic areas, and/or cities.Utilizing feature vectors, which can be adjusted based on user input orby other techniques, these entities can be compared to each other andranked with respect to each other. Additionally, embodiments of thepresent invention utilize user inputs and cloud-based sources as inputsto the neighborhood comparison and ranking process, enabling improvedresults compared with conventional techniques. These and otherembodiments of the invention along with many of its advantages andfeatures are described in more detail in conjunction with the text belowand attached figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high level block diagram of an apparatus for processing andusing feature vectors for homes and/or neighborhoods according to anembodiment of the present invention;

FIG. 2 is a high level schematic diagram illustrating a neighborhoodranking system according to an embodiment of the present invention;

FIG. 3 is a high level schematic diagram illustrating characteristicsused in computing feature vectors according to an embodiment of thepresent invention;

FIG. 4 is a high level schematic diagram illustrating feature vectorcomparisons between a current city and a new city according to anembodiment of the present invention;

FIG. 5 is a high level flowchart illustrating a method of determining anarea having similar characteristics to a first geographic areaassociated with a user according to an embodiment of the presentinvention;

FIG. 6 is high level schematic diagram illustrating a computer systemincluding instructions to perform any one or more of the methodologiesdescribed herein;

FIG. 7 is a high level flowchart illustrating a method of suggesting anew neighborhood to a user based on a current neighborhood according toan embodiment of the present invention;

FIG. 8 is a high level flowchart illustrating a method of comparing acurrent neighborhood to a desired neighborhood in a city of interestaccording to an embodiment of the present invention; and

FIG. 9 is a high level flowchart illustrating a method of providing auser with a neighborhood ranking according to an embodiment of thepresent invention.

FIG. 10 is a high level flowchart illustrating a method of suggesting anew neighborhood to a user based information related to the user'sprevious neighborhoods according to an embodiment of the presentinvention.

FIG. 11 is a high level flowchart illustrating a method of suggesting anew neighborhood that has the same or similar classification of aselected characteristic as the user's current neighborhood according toan embodiment of the present invention.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Embodiments of the present invention relate to technologies tofacilitate homeowners and/or renters in finding, acquiring, insuring,and/or maintaining real property. Technologies related to embodiments ofthe present invention support a homeowner/renter, for example, a memberof the present assignee, with the initial preparation associated withthe purchase of a home and/or rental of a home/apartment. Such initialpreparation can include advice and counseling related to a person'sability to afford a home or apartment, development of a financial planto facilitate the acquisition, web-enabled self-service systems (e.g.,home purchase calculators) used to determine financial goals andrequirements, and/or on-line member communities related to homeownershipand/or rental.

After a person completes initial preparation, technologies related toembodiments of the present invention assist the person in findingsuitable properties through the use of rent/buy listings includinginformation tailored to each person's interests and background. Forexample, preapproval of the person for mortgage rates and/orhomeowner's/renter's insurance can be used to provide rich informationcontent as part of the search process. On-line member communities can beused to assist users in finding property that is suitable for theparticular user's interests and income.

Additionally, technologies related to embodiments of the presentinvention provide for assistance in the purchase/rental transaction,including obtaining a mortgage and provision of assistance innegotiating the purchase or lease. Protection of the newly acquired homeor rented property is also related to embodiments of the presentinvention, in one of several forms including homeowner's insurance,mortgage life insurance, renter's insurance, flood insurance, personalproperty insurance, home security systems, home warranties, and thelike.

Moreover, technologies related to embodiments of the present inventionprovide a person with assistance in moving to, maintaining and/orrenovating, and/or refinancing the newly purchased or rented property.Thus, embodiments of the present invention relate to technologies thatprovide a one-stop home resource for delivering home solutions relatedto buying, selling, renting, and/or owning real property. In particularembodiments, members of a membership organization (e.g., an insurancecompany, a financial institution, real estate company, or the like)utilize the methods and systems described herein to manage their realproperty interests and interact with other community members to enablenew concepts related to homes and other real property.

Realtors have information of use to home purchasers, includinginformation available through the Multiple Listing Service (MLS).Advances in information sources are reducing the information barrierassociated with real estate information. A characteristic of real estatetransactions is the complicated nature of the transactions involved.Thus, many real estate purchasers feel they benefit from assistanceprovided by realtors. Some systems implemented by the present assigneeassist home purchasers and sellers in navigating the offer/acceptancephase of the real estate transaction.

Embodiments of the present invention provide systems that enable a homepurchaser to obtain information related to neighborhoods of interest.Typically, a user interested in moving to San Antonio, Tex. whopossesses little information about the San Antonio area, will enter azip code or a city name into a real estate search engine to obtain alisting of homes for sale in the San Antonio area. Because of the lackof information available to the user, this search process is typicallytedious and inefficient. Embodiments of the present invention obtaininformation related to the user and information related to the area ofinterest and generate neighborhoods of interest to the user. In anexemplary embodiment, demographic data related to a first area known tothe user, for example, demographic data on schools, geohazards, cost ofliving, tax rates, traffic, proximity to banks, crime rates, policeservices, fire services, proximity to grocery stores, proximity tohospitals, proximity to sex offenders, and the like is analyzed todetermine the user's neighborhood profile. This neighborhood profile canbe related to a profile for a neighborhood in which the user has livedin or liked in the past, the neighborhood the user currently lives in,or the like.

If the user is living in a first city and is moving to a second city,the system can analyze information related to neighborhoods in the firstcity and generate neighborhoods in the second city that may be ofinterest to the user. For example, the system can analyze physicalcharacteristics of the first city, such as house values, school districtratings, crime statistics, 3G connectivity or other wireless services,access to highways, amenities, distance from the downtown core area, andthe like, and can determine similar neighborhoods in or near the secondcity. In addition to neighborhood characteristics, characteristics ofhomes can be included in the analysis, for example, number of bedrooms,home improvements, insurance rates, and the like. Moreover,characteristics of people living in the neighborhood (e.g., peopledemographics) may be utilized, including income level, median income,jobs, consumption patterns, utility patterns, number of cars per home,move frequency, military status, age/income/etc. of dependent childrenand/or spouse, environmental consciousness, and the like. The user mayprefer to live in a new neighborhood where people with similar peopledemographics as the current or past neighborhoods. Thus, by using theinformation related to the first city, a system in accordance with thepresent invention can generate neighborhoods in the second city for theuser with similar physical characteristics and people demographics asthe first city.

As an example, a user typically begins a search of the MLS based on acity, an address, or an MLS number. Entering a city can result in alarge and unmanageable number of listings. An address may not be knownbased on the user's lack of knowledge of the new city. It is unlikelythat the user will know the MLS number of a particular property. Inorder to assist the user in finding neighborhoods of interest andthereby homes of interest, embodiments of the present invention can usea person's current location, for example, obtained from a mobile devicewith location information, as a starting point for the home search.

The neighborhood like mine functionality provided by embodiments of thepresent invention provides an initial telescoping of a search based oninformation about a current city and/or information about the user,which can be matched to determine neighborhoods of interest. Accordingto embodiments of the present invention, information can be stored in adatabase or received from the user, including information related to aprevious neighborhood in which the user has lived. A previousneighborhood includes any neighborhood in which the user has lived,including past and current neighborhoods. As an example, previousaddresses may be maintained by a membership organization, may beobtained from a credit report, may be obtained based on data entry bythe user, or the like. Embodiments of the present invention obtain datarelated to the previous city, as well as the city to which the user isintending to move. The data is analyzed to form a metric (e.g., avector) for the previous city as well as neighborhoods in the futurecity. The system is then able to suggest a neighborhood of interest tothe user. In one embodiment, the system provides information to the userrelated to the difference between two neighborhoods, for example, thatmedian income for the new neighborhood is $5,000 less than the previousneighborhood, and the like. Based on the suggested neighborhood, theuser is able to perform a search for a particular home much moreefficiently.

In an embodiment, the user can enter several previous addresses at whichthey have lived in chronological order and optionally prioritize theaddresses. In other embodiments, a single address is entered. In otherembodiments, the addresses are automatically filled from a databaseincluding information on the user.

FIG. 1 is a high level block diagram of an apparatus 100 for processingand using feature vectors for homes and/or neighborhoods according to anembodiment of the present invention. As used herein, a feature vector isan n-dimensional vector of numerical features that can representcharacteristics of homes and/or neighborhoods. When representingcharacteristics of neighborhoods, the feature values may correspond tovarious numeric values, such as house values, crime rate, schooldistrict ratings, distance to parks, distance to other amenities, or thelike. Using a feature transform, the neighborhood or homecharacteristics are mapped onto a feature vector in an appropriatemultidimensional feature space. A good quality feature vector can beformed by appropriate analysis and selection of data to determine themakeup of each of the cells in the vector. The analysis may includeexamining clustering of data. In some embodiments, a dendrogram may becreated using clustering, and various clusters can be selected andtargeted to build a feature vector. In embodiments of the presentinvention, the similarity of two neighborhoods can be defined by theproximity of their feature vectors in the feature space. The closertheir feature vectors are located in the vector space, the more likelythe two neighborhoods associated with the feature vectors will besimilar to each other.

As illustrated in FIG. 1, user data is received as an input andneighborhood rankings are provided as an output in an embodiment of thepresent invention. Referring to FIG. 1, the feature vector system 110includes a data processor 112 operable to compute and adjust featurevectors, a feature vector comparison module 114, and a feature vectordatabase 116. The feature vector processor 110 also includes aninput/output module 120, a geographical database 130, and a housingdatabase 132. External databases 140 are accessible to the featurevector processor 110 and are utilized in some embodiments.

A number of different types of user data may be received by the featurevector system 110. The user data received as an input may include a newgeographical area (e.g., metropolitan area, city, zip code, subdivision,school district or the like) to which the user is interested in moving.Other user input may include the user's preferences related to variouscharacteristics of neighborhoods or homes (e.g., proximity to work,proximity to school, school ranking, or the like). The user data mayalso include personal data or financial data of the user. Alternativelyor additionally, some of the user data may be automatically populated byother systems associated with feature vector system. As described fullythrough the present specification, the input/output module 120, the dataprocessor 112, feature vector comparison module 114, feature vectordatabase 116, geographical database 30, housing database 132, andexternal database 140 are utilized to receive inputs from a useroperating user a computer and determine neighborhood rankings in a newgeographical area. In addition to providing neighborhood rankings, thefeature vector system can also provide ranking of individual homeswithin a new neighborhood that best match the user's preference.

The external databases 140 may include a variety of third party datasources which may be publicly available or proprietary data which may becommercially available. For example, the external databases may includecensus data including average household income, average household numberand size, diversity of population related to age, ethnicity, race, orthe like. Also, property tax information, neighborhood association fee,home appraisal values of homes in a neighborhood, or the like may bestored in external databases and can be used for computing a featurevector associated with a neighborhood. Proprietary data may includeinformation related to people who lived in particular houses, propertyownership history, spending habits of people in the neighborhood, andthe like.

Although external databases 140 are illustrated in FIG. 1, this is notrequired by embodiments of the present invention. In some embodiments,information necessary for feature vector analysis and/or neighborhoodranking is maintained internally within the feature vector system 110.In some embodiments, data from both internal and external sources isintegrated to provide the system operator with data that is both usefuland low in cost, however, this is not required by the present invention.

FIG. 2 is a high level schematic diagram illustrating a neighborhoodranking system according to an embodiment of the present invention. Theneighborhood ranking system 200 includes a feature vector engine 210operable to compute, adjust, and store feature vectors. The featurevectors may be stored in the real estate database 220 or thegeographical information database 230. The neighborhood ranking system200 also includes a comparison/ranking engine 250 that can comparefeature vectors for various geographic areas and rank the geographicareas using the comparisons. A feature vector display 222 and aneighborhood rankings display 232 are provided and interact with a userinput module 240 and a user output module 242 to provide forinput/output functionality. In an embodiment, the user input and outputare provided through one or more web pages accessed through theInternet.

The user is able to enter information used by the neighborhood rankingsystem, for example, by the feature vector engine 210. As described morefully in relation to FIG. 3 below, the feature vector engine 210receives information about the user's current neighborhood, pastneighborhood, or notional neighborhood desired by the user. The featurevector engine 210 may also receive information about particular featuresof an individual home currently owned or desired by the user, and thelike. The feature vector engine 210 further receives input related to anew geographical area of interest. Additionally or alternatively, thecomparison/ranking engine may receive user input related to a newgeographical area of interest. Based on characteristics of neighborhoods(or homes), a feature vector associated with each neighborhood can beperformed by the feature vector engine 210. Information related to eachneighborhood, represented by a feature vector, can be presented to theuser using feature vector display 222.

Information used by the comparison/ranking engine 250 may be provided bythe user using the user input module 240 or may be provided as a resultof feature vector analysis performed by the feature vector engine 210.As described in relation to FIG. 3, feature vectors associated with oneor more neighborhoods in a new geographical area may be retrieved from adatabase and may be compared with a feature vector selected by the useror the system (e.g., a feature vector associated with the user's currentneighborhood or a notional neighborhood desired by a user). A distancebetween each of the feature vectors associated with new neighborhoods iscompared with the selected feature vector. The neighborhoods in the newcity or geographical area of interest can be ranked according to thedistance between each of the feature vectors associated with newneighborhoods and the selected feature vector. The neighborhoodcomparison and ranking of the new neighborhoods is displayed using theneighborhood rankings display 232.

Thus, embodiments of the present invention provide functionality notavailable using conventional neighborhood or home search systems.Embodiments of the present invention utilize feature vectors tocharacterize homes, neighborhoods, geographic areas, cities, ordemographics in different areas. Utilizing feature vectors, which canadjusted based on user input or by other techniques, any number ofcharacteristics associated with these entities can be compared to eachother and ranked with respect to each other. In addition, sincecharacteristics associated with these entities can be weighted accordingto their importance to the user, the search results will better reflectthe preference of the user.

Embodiments of the present invention can be categorized into at leastfour facets of the neighborhood or home search process. Each facet canhave variations. The first facet is to show the user neighborhoods thatare like ones that the user likes, typically expressed by a set ofcriteria for things that the user likes (i.e., a notional neighborhood).The user has not lived in this notional neighborhood, but knows theirpreferences and the kind of neighborhood they prefer. The criteria orpreferences can include living in close proximity to a location, forexample, school, work, shopping, and the like.

The second facet is based on neighborhoods like the one that the userlives in now. Attributes that define the current neighborhood can beused as inferred attributes based on demographics and/or expressedattributes similar to the preferences discussed in relation to the firstfacet.

The third facet is based on neighborhoods like the one that the userused to live in, where this history may be stored in a databasemaintained by the system operator or may be obtained from other sources.The demographic information for the previous neighborhoods can be usedand combined with 1 to N criteria expressed by the user, for example,proximity of churches, proximity of schools, and the like. In someembodiments, a series of homes are included in the analysis so that atrend can be developed for the user and the neighborhood suggestionsutilize this trend in determining neighborhoods of interest to the user.

The trend developed for future neighborhood suggestions may depend oncharacteristics of the user's previous neighborhoods as well as theuser's personal data and financial data. Depending on the user's age andfamily status, some aspects of a suggested future neighborhood may bedifferent from the previous neighborhoods. As an example, if the user isabout to become an empty nester with children leaving for college, thencertain attributes of a suggested future neighborhood, such as schooldistrict ratings or average home size, may be different fromcorresponding attributes in the previous neighborhoods. In anotherexample, if the user's financial data over time reflects that the user'sincome has been increasing at a steady rate, then the user's financialdata may be taken into account in determining affordability homes in asuggested future neighborhood. The user's personal data and/or financialdata can be incorporated as part of vector analysis to compute a featurevector associated with a future neighborhood for the user.

The fourth facet is based on neighborhoods that people like the userlike. In this embodiment, a demographic overlap between the user and thedemographics of the people living in the neighborhoods is used tosuggest a neighborhood including people with a similar demographicprofile to the user. The demographic information can include behavioralaspects (e.g., the user has a checking account and owns a '76 BuickRegal) and personal information (e.g., the user is 26 years old, gender,ethnicity race, or the like). The demographic information can alsoinclude people's income, political association, family status, or thelike. The system is able to systematically infer that there are othersimilar people in a particular neighborhood and suggest such aneighborhood. As an example, if a user, seeking for a suitableneighborhood in a new city, has a family with young children, then thesystem can suggest a neighborhood in a new city where people with asimilar family profile live in.

As an alternative to suggesting a neighborhood with homes for sale, thesystem could recommend that the user rent a home/apartment. For example,if the user is a 26 year old young professional moving to Chicago, thesystem could recommend that the user rent rather than buy a house, sincemost of the people in the user's situation don't buy houses. The systemcould then recommend rental properties in neighborhoods of interest thatmatch one or more of the facets discussed above.

Embodiments of the present invention can apply customer segmentationapproaches to neighborhood segmentation. Neighborhood segments can bedefined based on characteristics of the people in the neighborhood.Neighborhoods in the user's city of interest can be divided intodifferent segments using a suitable segmentation technique. For example,the neighborhood segmentation process can include dividing the entirecity based on characteristics of people living in each neighborhoodsegment (e.g., income, age, family status, or the like). Onceneighborhood segments are defined, a correlation between a person'ssegment and the neighborhood segment can be used in suggestingneighborhoods of interest.

Data used in performing the neighborhood segmentations can be providedby users based on social media or other suitable techniques. A personliving in a neighborhood could provide data inputs used by the systemdescribed herein. Thus, the system provides an input portal throughwhich a user can enter information on a particular neighborhoodincluding likes and dislikes associated with the neighborhood (e.g., Ilike the schools, I don't like the fact that it is so far from theairport or so far from shopping, I really like the proximity to parks,etc.). The preferences could be ranked on a scale, for example, from oneto ten, and as the amount of user entered data increases, demographicinformation can be combined with user-generated information such as thatfrom social media sites to improve the quality of the vectors associatedwith a particular neighborhood. As an example, an embodiment of thepresent invention utilizes a vector-based approach in which theinformation on the user is vectorized in an N-dimensional space and theinformation on the neighborhood is vectorized in an M-dimensional spacein order to compare the vectorized information. In a particularembodiment, N=M. The vectors can be thought of as a DNA vector built foreither the user, a home(s), or neighborhood(s). The comparison processincludes finding vectors that match most closely, differ by less than apredetermined percentage, or the like.

Other embodiments utilize both conventional neighborhood definitionsand/or neighborhood definitions that will have non-standard geographicboundaries based on the people living in the neighborhood. As anexample, a neighborhood could be defined by a set of Zip+4 zip codes, bycity, or the like. The neighborhoods could also be defined bydemographics.

In an embodiment of the present invention, a neighborhood boundary canbe defined by the user input. The user can geo-fence geographic areas ofinterest to the user. For example, the user's computing device candisplay a map of a city or a metropolitan area that the user isinterested or display a map of an area that the user is currentlygeo-located by the user's mobile computing device. On a display screen,the user can draw freehand a boundary around geographical areas withfeatures that the user like with a pointing device. A boundarygeo-fenced by the user can be regarded as a neighborhood for the purposeof computing a feature vector for the geographic area, even if itincludes a geographic area larger than what is typically regarded as aconventional neighborhood. In some embodiments, the user can geo-fencearound non-contiguous areas on the map and define them as a singleneighborhood to compute a feature vector associated with a neighborhoodgeo-fenced by the user. For example, the user can geo-fence around aportion of one conventional neighborhood and combine it with a portionof another conventional neighborhood. Thus, neighborhoods can be userdefined or defined by a feature vector system according to embodimentsof the present invention.

In another embodiment of the present invention, the user's geo-locationmay be used to compute a feature vector for a neighborhood around theuser's current geo-location. The user may serendipitously uncover ageographic area with many features that appeal to the user. Although theuser has no intention of moving to this particular neighborhood, theuser may desire to find a neighborhood in a different city which hascharacteristics similar to the neighborhood that the userserendipitously stumbled upon. In an embodiment of the presentinvention, the user may be geo-located by the user's mobile device, anda map of geographic areas around the user's geo-location may bedisplayed on the screen of a mobile computing device. A mobileapplication on the user's mobile computing device may provide aninteractive graphical user interface for user input. For example, theuser may draw a boundary on a map shown on the display screen,geo-fencing specific streets, subdivisions, or the like and define thegeographic area within the boundary as a neighborhood. The user may alsoprovide free text input with respect to features of the neighborhoodthat appeal to the user. Based on the user input and third partyinformation (e.g., demographic profiles, home price, crime rate, and thelike), a feature vector of the neighborhood around the user's currentlocation can be calculated and saved in a database for comparison withother feature vectors.

In yet another embodiment of the present invention, a mobile computingdevice may have an application software that links the person's currentlocation with a search engine for homes in an nearby neighborhood. Whena user passes by a new geographic area and finds a neighborhood withcharacteristics that the user like, then the user can geo-locate theuser's position with the user's mobile computing device (e.g., a mobilephone). The user can use a mobile application operated by an entity(e.g., a real estate company, a financial institution, or an insurancecompany, or the like) to view a map of a neighborhood on a graphicaluser display, including street names and landmarks, at which the user isgeo-located. The mobile application may also provide a link on thegraphical user display so that the user can review information about theneighborhood provided by third parties. The mobile application may alsoprovide a search function for the user to initiate a search for homesavailable for sale or for rent in the neighborhood.

FIG. 3 is a high level schematic diagram illustrating characteristicsused in computing feature vectors according to an embodiment of thepresent invention. As illustrated in FIG. 3, data or characteristicsrelated to an entity are shown in various groups. As an example,attributes associated with a neighborhood are illustrated: propertyvalues, schools, and crime. These attributes are merely exemplary. Datafor each attribute is illustrated as points lying in the attribute plane(i.e., dots for property values, dots in circles for schools, andsquares for crime). Based on these data points, a feature vector 310 canbe computed for the exemplary neighborhood. Although the property valuesand school quality are somewhat spread out, the crime data is tightlyclumped, for example, illustrating that this particular neighborhood ischaracterized by low crime.

Some embodiments of the present invention adjust for differences in thecost of living in different areas. As an example, in a low cost ofliving area, a 5-bedroom house can be purchased for $300,000, but in ahigh cost of living area, a 1-bedroom condominium can be purchased for$300,000. The system can consider cost of living differences includingtaxes at the city and state level, and the like.

FIG. 4 is a high level schematic diagram illustrating feature vectorcomparisons between a current city and a new city according to anembodiment of the present invention. In FIG. 4, a feature vector for acurrent city illustrates that the median neighborhood has a propertyvalue in a first range (M₁) and a user's neighborhood is above themedian, with a spread of Δ₁. In a new city, with a lower cost of living,the median neighborhood has a property value in a reduced range (M₂),with a spread of Δ₂.

Embodiments of the present invention integrate the search functionalitydescribed herein with the user's personal financial situation, which canbe identified using personal financial management (PFM) software. PFMsoftware can aggregate all your financial data, provide the ability tobudget, the ability to plan, the ability to set goals for saving, andthe like. The search results can be combined with the user's financialcondition in order to further filter the search results that areapplicable to the user's situation, for example, homes that are in theuser's price range based on their financial condition.

According to an embodiment of the present invention, a user provides anaddress (e.g., an address of the user's current home) and a demographicanalysis is performed in relation to the address. The demographicanalysis can produce demographic information using the address, aneighborhood in which the address is located, a user-defined geographicarea including the address, or the like. The embodiment also includesreceiving an input of a new city. Demographic information related to thenew city (e.g., demographic information related to neighborhoods in oradjacent to the city, homes in or in the vicinity of the city, etc.) iscompared to the initial demographic information to predict a newneighborhood that is well correlated with the user's current home and/orneighborhood.

According to some embodiments of the present invention, the four facetsdiscussed above can be combined, for example, use of the user's currentaddress, a demographic analysis based on information stored about theuser, and a set of criteria or preferences. As an example, a set ofslider bars could be pre-populated with default values based on theuser's current neighborhood. The user could then modify the slider barsto provide preference information (e.g., number of parks, quality ofschools, access to the highway, access to the airport, and the like). Insome situations, the user is moving to a completely different type ofcity (e.g., from a small town to New York City), and the analysis of theuser's current neighborhood may not necessarily be useful in determininga type of neighborhood that the user desires in a city. The combinationof an analysis of the current address or demographics and the expresspreferences of the user can be utilized to suggest potentialneighborhoods of interest in the new city.

In other embodiments, a neighborhoods search with slider bars canproduce results that are then combined with the PFM data on the user topredict whether or not the user can afford the homes returned in thesearch result. In some embodiments, using the user's PFM data, theaffordability of the homes can be determined based on the user's monthlyincome or cash flow and combined expense, including a mortgage payment,insurance, association fee, home maintenance fee, or the like. If it isdetermined that the user cannot afford a home with features that areimportant to the user, then the system may suggest renting or maysuggest a nearby city that is more affordable or has features that arebetter aligned with the user's preferences.

A membership organization implementing embodiments of the presentinvention can utilize information on other members in determining thevectors associated with the user and the neighborhoods. As an example,in a particular neighborhood, a separate vector can be computed forother members living in that neighborhood. Additionally, this analysiscould be performed on the neighborhood that the user is living incurrently. Thus, the demographic data for the neighborhood could besupplemented by demographic data for members of the organization.

An additional source of data for a membership organization could berecords on a particular property occupied by members as a function oftime. A membership organization can include a company with a number ofcustomers, for example, a bank with a state-wide or national footprint.As an example, if 123 Main Street was owned by a first member at a firsttime and a second member at a second, later time, the demographics ofthe neighborhood could be tracked as a function of time. Thus, ademographic profile of that house can be constructed based on themembers that owned the house over time. For example, a house could bewell suited for a junior officer who is a recent graduate of a serviceacademy since several members fitting this profile have owned the home.The information could be weighted as a function of time to improve therelevance of the data. The information maintained by the membershiporganization can be supplemented with information from other sources,including information on insurance claims for a particular home. Inaddition to information on homes for sale, information on rentalproperties can be utilized as well.

In an embodiment of the present invention, information on the user canbe obtained from several sources including behavioral information suchas spending, income, family size, profession, work experience, and thelike. This information can be paired with third party segmentation datathat places people into a predetermined number of segments. In addition,user supplied data can be utilized. As an example, if a user is a dogowner, they could rate the neighborhood in respect to issues related todog ownership. Accordingly, a user looking to move into thatneighborhood would be able to use the user supplied data to enhance thesearch results. Social media inputs can be used by the users to provideuser supplied data.

The ratings on the neighborhoods can be sorted based on the source ofthe data used in the analysis, for example, showing a user all theopinions on the neighborhood, or only opinions from people that actuallylive there or have lived there, or all opinions, even for people whochose not to live in that particular neighborhood. In another example,the source of data used in the analysis maybe limited to a socialnetwork of friends or experts whose opinions the user trusts, regardlessof whether they actually live there or have not lived there. As membersprovide reviews of neighborhoods, the data can be aggregated or pooledand the words that were used in relation to a given neighborhood can beanalyzed to determine neighborhood characteristics. Word analysis can beused to determine the neighborhood characteristics: good schools,churches, good traffic, and the like. If the user is currently living ina neighborhood that has neighborhood characteristics defined by thecloud of reviews, what people have said about the neighborhoods in thenew city can be used in performing the neighborhood matching.

An alternative embodiment of this concept is a user entering a name of aneighborhood and the output being provided in the form of the wordanalysis (cloud analysis) that describes the way that people in theaggregate have characterized the neighborhood. Embodiments of thepresent invention enable the combination of structured data andunstructured data to provide neighborhood characterizations. As anexample, for crime, structured data in the form of crime statistics areavailable and can be used to characterize a neighborhood as a low crimearea. In addition to this structured data, data from the cloud can beused to supplement the structured data or in place of the structureddata. If, for a particular neighborhood, significant discussion isrelated to the absence of crime, even though the word “crime” wouldappear frequently, access to the structured data would indicate that theneighborhood discussion of crime is actually a good thing, since theneighbors are discussing how little crime there is in the neighborhood.

A variety of data sources can be utilized with embodiments of thepresent invention including census, tax record information, which canprovide information about the appraised value of the property, which, inturn, provides information about the cost of living in the neighborhood,and zoning information. Census data for a given property providesinformation about the number of people living in the house, theirincome, and the like, which is not available through tax records. Thedata would be useful, not only to potential buyers, but to renters aswell. Other data fields associated with the census can also be used. Thedata discussed above can be overlaid with third party data that may bepublicly available. First party proprietary data can includeinformation, discussed above, on the people who lived in particularhouses, property ownership history and the like.

In relation to zoning information on the house of interest, zoninginformation for adjacent homes could be utilized, for example, for ahome that is zoned single-family but abuts multi-family zonedproperties, the zoning information could be used to characterize theneighborhood. Thus, neighborhoods can be defined at smaller units thanthe conventional units utilized by the MLS, providing higher granularitythan available using conventional approaches.

In some embodiments, a N-dimensional vector (also referred to as afeature vector) is computed that characterizes the user, the user'scurrent home, and/or the user's current neighborhood based on theschools, the proximity to the highway, the parks close to theneighborhood, median income, number of children, and the like. Vectorscan be created for neighborhoods or for individual houses. Thedefinition of a neighborhood may be performed by creating N-dimensionalvectors for each home in the area and when a predetermined number ofhomes have vectors matching the vector of the user/original neighborhoodwithin a predetermined range, the neighborhood is defined based on thematching homes. In this sense, the analysis provides not only“neighborhoods like mine,” but “houses like mine.” In some searchresults, a predetermined number of “top hits” can be returned of houseswith vectors matching the user/original neighborhood vector. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

Some embodiments define a neighborhood as a geographic polygon includinghouses with vectors matching the search vector. In some embodiments, theneighborhood vectors and the home vectors are inter-dependent. Theneighborhoods defined by embodiments of the present invention caninclude a polygonal shape that can include one or more MLSneighborhoods, one or more school districts, or the like. For example, aneighborhood can be defined according to a school district, even if itspans across different slivers or non-contiguous areas using ageographic polygon. Since the neighborhoods can be defined based on thedemographic data analysis or other techniques disclosed herein, theneighborhoods are not limited to conventional neighborhood definitions.

If homes are matched against the search vector, the search results willbe returned in a form similar to an N-dimensional bell curve, with themost closely matched home at the peak of the bell curve. Homes thatmatch less closely are positioned down the bell curve toward the wings.At a threshold level of dissimilarity, the neighborhood is defined.Thus, using a home as a starting point provides additional methods ofdefining neighborhoods, which may not include a contiguous geographicarea. The static neighborhood definition can be modified into aneighborhood defined by vector similarity, not geographical boundaries.

Alternatively, the vector for the user/neighborhood could be computed. Adatabase maintained by the system operator, for example, a membershiporganization, could then be accessed to determine one or more memberswith vectors closely matching the user/neighborhood vector. AN-dimensional bell curve would be created around the homes of the one ormore members to define the neighborhood of interest.

In these embodiments, rather than characterizing neighborhoods based onMLS definitions, zip codes, or the like, other people with a vectorsimilar to the user are found in order to determine neighborhoods ofinterest to the user. Other people with a vector similar to the user mayinclude those who have a similar demographic profile, who searched orviewed the same neighborhood of which the user is interested. When theuser's search results in a particular neighborhood, the system may alsosuggest additional neighborhoods that other people with a vector similarto the user may have searched or reviewed. Basing the vector on otherpeople can be combined with expressed preferences and the like.

The N-dimensional vector comparison discussed herein differs from makingcomparisons based on numerical ranking values and provides for morecustomized results in comparison with a single numerical value. TheN-dimensional vector computed can be referred to as a feature vector andcan describe an area that the user would want to live. Categorical datacan be included in the analysis, for example, does an area have goodamenities, which can be weighted to give a number for each amenity. Theweights will indicate the importance of the various amenities orcharacteristics to the user. Each vector can be described by a distancemetric compared to a reference feature vector. The distance metric(e.g., a Euclidian distance) can be used to find locations within athreshold distance of the reference feature vector. The distance metricis not a physical distance (e.g., meters), but a vector distanceassociated with the similarity between the feature vectors. One ofordinary skill in the art would recognize many variations,modifications, and alternatives. As the weights on the variouscharacteristics are changed, the reference feature vector changes inresponse, modifying the distance metrics for other vectors compared tothe reference feature vector. The distance metric will relate to thedistance in vector space from the reference feature vector, for example,neighborhoods within 5% of the reference feature vector.

The neighborhood analysis can include performing a cross-section of thehouses in a neighborhood to obtain an approximate average of the value,average lot size, average garage size, average distance to the highway,and other factors to generate a profile of the user's current house anduser's neighborhood.

A baseline feature vector can be computed for a metropolitan area andthen a determination can be made of how each neighborhood's featurevector departs from the baseline feature vector. In this analysis, asuburb of a city would be compared to the metropolitan area as a wholeto determine how the particular neighborhoods depart from the baselineassociated with the metropolitan area. In finding a new home in the newcity, the search could focus on finding a neighborhood in the newmetropolitan area that departs from the baseline in a similar manner tothe departure in the old metropolitan area.

Embodiments of the present invention can provide the user with a usefulcomparison between metropolitan areas. As an example, a neighborhood inthe new metropolitan area could be matched to the user's currentneighborhood. Then a house in the new neighborhood could be found thatis the closest match to the user's current house. The system would showthe price differential, among other factors, between the current houseand the new house. A user interface could be provided that will show theuser the changes in the characteristics as the price of the new home isadjusted. For example, as the target price range for the new house israised, changes in the schools, traffic, and the like could be displayedin response to the increase in the target price range.

Some embodiments define the neighborhood based on a comparison of thecharacteristics that are most important to the user. As an example, auser could specify a five-bedroom house, with A+ schools, close to abank, and close to their work. The system could generate one or moreneighborhoods that matched the criteria within a predeterminedthreshold. A matching metric could indicate how closely theneighborhoods matched the criteria and guide the user in finding homesmost closely matching their criteria.

According to some embodiments of the present invention, a comparisonbetween a current home and a similar new home in a new city is provided,including a scaling between the current home and the new home. As anexample, the scaling could indicate an increase in price for the newhome in comparison with the current home, an increase in the price ofinsurance for the new home in comparison with the current home, and thelike. In addition to comparisons between a new home and a user's currenthome, the user could specify the features desired in a current home andthis notional home could be used as the baseline home. Thus, a currenthome can be ranked against a potential new home and housing attributesfor the current home and the new home can be ranked in comparisonmanner.

According to an embodiment of the present invention, a Euclidiandistance between a current home and a median home for the current cityis computed. The distances for the various measures (number of bedrooms,quality of schools, etc.) are computed and then can be adjusted by theuser. As an example, if a current home is 10 miles from an elementaryschool and the user wants a home that is 5 miles from the elementaryschool, the variable for distance to local school could be modified bythe user to provide a search vector based on the current home, buttailored to the user's tastes. The attributes can be weighted based onuser preferences to provide an index that combines the attributes andthe weighting to rank homes in comparison with each other.

In order to obtain information about a user, embodiments of the systemprovide a free text entry interface that enables a user to enter freeform text describing aspects that the user prefers in relation to formerhomes or interests. Based on the text entries, the system constructs atag library of words that are significant for a new house, for example,close to the ocean, next to the highway, close to work, water, schools,low crime, shopping, theaters, and the like. A statistical model is thenused in conjunction with the free form text to generate an initialsearch vector for a new home. Sentence analysis could be used tosupplement the free text search. It is possible that inputs for the freetext entry interface will be provided using history information for theuser, social networks, and the like. The word-based inputs can be usedin creating the feature vectors described more fully throughout thepresent specification.

According to some embodiments, information related to the user, forexample, the user's current home, the user's current neighborhood, andthe like are used to construct a feature vector for the user. Then thetags associated with the user are used to modify the feature vector andcompute an updated feature vector that is based on current informationas well as user input.

According to an alternative embodiment of the present invention, theneighborhood definition is provided by the user. In this alternativeembodiment, the user will specify the geographic boundaries of an areathat is defined as the user's neighborhood. In contrast withconventional neighborhood definitions based on realtor maps and thelike, the user can define an arbitrary neighborhood based on their owngeographical boundary definitions. The geographic boundary defined bythe user will then be used to create a feature vector for the userdefined neighborhood for use in determining similar neighborhoods in anew city. In an exemplary embodiment, data on the homes within theuser-defined neighborhood is used to provide home attributes used increating the feature vector.

FIG. 5 is a high level flowchart illustrating a method of determining anarea having similar characteristics to a first geographic areaassociated with a user according to an embodiment of the presentinvention. The method 500 includes receiving a plurality of inputsrelated to characteristics of a plurality of geographic areas (510). Theplurality of geographic areas can be associated with neighborhoodslocated in or adjacent to a city of interest and the characteristics canalso be referred to as attributes, such as the quality of schools,access to public transportation, and the like. The method also includesconstructing a feature vector for each of the plurality of geographicareas based on the plurality of inputs (512). The feature vector is anN-dimensional vector that characterizes the geographic area (e.g., aneighborhood) in a quantitative manner. The N-dimensional vector can bedefined using N characteristics associated with the first geographicarea. The method further includes receiving a plurality of inputsrelated to characteristics of the first geographic area (514) andconstructing a feature vector for the first geographic area (516). Insome embodiments, the feature vectors for the plurality of geographicareas and/or the feature vector for the first geographic area can beadjusted prior to a subsequent comparison step as described below.

Moreover, the method includes receiving an input from the user relatedto a city of interest (518), comparing the feature vector for the firstgeographic area to feature vectors associated with geographic areaslocated in or adjacent to the city of interest (520), and ranking thegeographic areas located in or adjacent to the city of interest usingthe comparing step (522).

It should be appreciated that the specific steps illustrated in FIG. 5provide a particular method of determining an area having similarcharacteristics to a first geographic area associated with a useraccording to an embodiment of the present invention. Other sequences ofsteps may also be performed according to alternative embodiments. Forexample, alternative embodiments of the present invention may performthe steps outlined above in a different order. Moreover, the individualsteps illustrated in FIG. 5 may include multiple sub-steps that may beperformed in various sequences as appropriate to the individual step.Furthermore, additional steps may be added or removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

FIG. 7 is a high level flowchart illustrating a method of suggesting anew neighborhood to a user based on a current neighborhood according toan embodiment of the present invention. The method 700 includescomputing a current feature vector for the current neighborhood (710).The current feature vector is based on a plurality of characteristics ofthe current neighborhood. The method also includes receiving an inputfrom the user related to a city of interest (712) and retrieving aplurality of feature vectors from a database storing feature vectorsassociated with neighborhoods in the city of interest (714). The methodfurther includes comparing the current feature vector with each of theplurality of feature vectors (716) and determining a neighborhood in thecity of interest defined by a minimum distance between the currentfeature vector and one of the plurality of feature vectors (718).

According to some embodiments, the method additionally includes rankingthe neighborhoods in the city of interest by increasing distance betweenthe feature vector for the current neighborhood and the plurality offeature vectors. Although the feature vectors for the neighborhoods areretrieved from a database in the embodiment illustrated in FIG. 7, thisis not required by embodiments of the present invention. These featurevectors can be computed in real time based on the user's input relatedto the city of interest. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

It should be appreciated that the specific steps illustrated in FIG. 7provide a particular method of suggesting a new neighborhood to a userbased on a current neighborhood according to an embodiment of thepresent invention. Other sequences of steps may also be performedaccording to alternative embodiments. For example, alternativeembodiments of the present invention may perform the steps outlinedabove in a different order. Moreover, the individual steps illustratedin FIG. 7 may include multiple sub-steps that may be performed invarious sequences as appropriate to the individual step. Furthermore,additional steps may be added or removed depending on the particularapplications. One of ordinary skill in the art would recognize manyvariations, modifications, and alternatives.

FIG. 8 is a high level flowchart illustrating a method of comparing acurrent neighborhood to a desired neighborhood in a city of interestaccording to an embodiment of the present invention. The method 800includes computing a current feature vector based on characteristicsassociated with the current neighborhood (810), receiving the selectionof the city of interest from the user (812) and accessing a plurality offeature vectors from a database (814). The characteristics associatedwith the current neighborhood can include characteristics associatedwith the user, for example, income, age, number of children, and thelike. The characteristics associated with the current neighborhood canalso include characteristics associated with homes in the neighborhood,such as average value, square footage, and the like. Moreover, thecharacteristics associated with the current neighborhood canadditionally include characteristics associated with neighborhoodfeatures, for example, number of trees per acre, proximity to a body ofwater, and the like. Each of the plurality of feature vectors isassociated with one of the plurality of neighborhoods in the city ofinterest.

The method also includes determining a plurality of neighborhoods in thecity of interest characterized by a minimum difference between thecurrent feature vector and each of the plurality of feature vectors(816) and displaying the plurality of neighborhoods to the user (818).The method further includes receiving a selection of a desiredneighborhood from the user (820), computing a difference between thecurrent feature vector and the feature vector associated with thedesired neighborhood (822) and displaying the difference to the user(824). Displaying the difference can include displaying a costdifferential between a similar home in the current neighborhood and thedesired neighborhood.

It should be appreciated that the specific steps illustrated in FIG. 8provide a particular method of comparing a current neighborhood to adesired neighborhood in a city of interest according to an embodiment ofthe present invention. Other sequences of steps may also be performedaccording to alternative embodiments. For example, alternativeembodiments of the present invention may perform the steps outlinedabove in a different order. Moreover, the individual steps illustratedin FIG. 8 may include multiple sub-steps that may be performed invarious sequences as appropriate to the individual step. Furthermore,additional steps may be added or removed depending on the particularapplications. One of ordinary skill in the art would recognize manyvariations, modifications, and alternatives.

FIG. 9 is a high level flowchart illustrating a method of providing auser with a neighborhood ranking according to an embodiment of thepresent invention. The method includes receiving an identification of acity including a plurality of neighborhoods (910), receiving a listingof homes located in the city (912), and parsing the listings intosubsets of listings (914). Each subset is associated with oneneighborhood. The indication of the city can be received as an inputfrom the user through a graphical user interface. The method alsoincludes computing, using a processor, a feature vector for the homes ineach subset of listings (916) and for each neighborhood, forming aneighborhood feature vector by combining the feature vectors for thehomes in each subset of listings (918).

The method further includes computing a current feature vectorassociated with a current home of the user (920). In the illustratedembodiment, the current feature vector is an N-dimensional vector basedon N attributes associated with the current home. The methodadditionally includes comparing the current feature vector with theneighborhood feature vectors for each neighborhood, and ranking theneighborhoods using the comparison (922). The current feature vector isa function of attributes associated with the current home. Additionally,the method includes providing the user with the ranking.

In some embodiments, the current feature vector is adjusted using userinput prior to comparing the current feature vector with theneighborhood feature vectors for each neighborhood. Thus, the user canmodify the feature vector, if, for example, the user wants a comparisonbased on a home larger than the user's current home. Other types of userinput can be used to adjust the current feature vector prior to thecomparison step. Certain characteristics of the current neighborhood(e.g., crime rate, school ranking, commute distance to work) may be moreimportant to the user than other characteristics (e.g., a number ofparks, distance to shopping centers, and the like). The user may provideinput related to a weighting factor for each of characteristicsassociated with the current neighborhood prior to computing a currentfeature vector so that the feature vector properly reflects importanceof various characteristics of the current neighborhood to the user.

It should be appreciated that the specific steps illustrated in FIG. 9provide a particular method of providing a user with a neighborhoodranking according to an embodiment of the present invention. Othersequences of steps may also be performed according to alternativeembodiments. For example, alternative embodiments of the presentinvention may perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 9 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

FIG. 10 is a high level flowchart illustrating a method of suggesting anew neighborhood to a user using the user's previous neighborhoodsaccording to an embodiment of the present invention (1000). The user'sprevious neighborhoods may include a neighborhood of the current home orneighborhoods of past homes in which the user has resided. Thecharacteristics associated with each of the user's previousneighborhoods may reflect the user's preference for certain types ofneighborhoods. Alternatively, changes in characteristics of previousneighborhoods over time may reflect a trend of the user's preference forcertain types of homes or neighborhoods over time. In an embodiment, theuser's previous neighborhood information can be obtained from the userusing the user input module 240. Alternatively, the user's previousaddresses stored in a database can be retrieved, and previousneighborhoods surround the previous addresses can be analyzed.

In determining the user's previous neighborhoods, the neighborhoodboundary may be selected using any suitable methods. In someembodiments, the boundary of a previous neighborhood can be provided bythe user. For example, a map of geographic areas surrounding the user'sprevious home may be displayed on a screen of the user's computingdevice. Using a mouse or other pointing device, the user may manuallydraw a boundary around a geographical area that the user regards as aprevious neighborhood surrounding the user's previous home. The boundarymay be of any suitable shape and may include one or more ofnon-contiguous areas. In other embodiments, the boundary of a previousneighborhood can be generated by a feature vector processor. As anexample, the city in which the user's previous home is located may bedivided into separate neighborhoods based on geographical features,clustering of homes, average home price, or the like. A processorgenerated boundary may be used to define boundaries around the user'sprevious neighborhoods.

As shown in FIG. 10, the method (1000) includes computing a timelinefeature vector for each of the user's previous neighborhoods, each ofthe user's previous neighborhoods having a plurality of characteristics(1010). A timeline feature vector is calculated using the plurality ofcharacteristics associated with each of the user's previousneighborhoods. The plurality of characteristics may include average homesize, average home price, average lot size, crime rate, school districtratings, distance to highway, distance to parks, or the like. Thesecharacteristics can be represented as numerical feature values, fromwhich a timeline feature vector can be constructed. Sincecharacteristics of one neighborhood differ from those of anotherneighborhood, each of timeline feature vectors calculated from theprevious neighborhoods is unique and different from one another.

The method (1000) also includes determining a combination timelinefeature vector predictive of the user's preference for a newneighborhood using one or more of the timeline feature vectors (1012).In an embodiment, a combination timeline feature vector may be aweighted average of the one or more timeline feature vectors. The usermay use a graphical user interface with options to rank the previousneighborhoods in which the user has lived according to the user'spreference for each neighborhood. The ranking of the previousneighborhoods may be used to compute a relative preference, or aweighting factor, associated with each timeline feature vector forprevious neighborhoods. From the weighted average of timeline featurevectors, a combination timeline feature vector can be determined.Additionally or alternatively, the user may be provided with an optionto describe, in free text mode, positive or negative experience livingin each of the previous neighborhoods. The free text input provided bythe user may be analyzed by a processor to determine a relativepreference of the previous neighborhoods by the user. The relativepreference based on the user's free text input may be used to as anweighting factor in determining a combination timeline feature vector.In other embodiments, a weighting factor may be determined by a dataprocessor, without the user input, based on a trend of the user'sprevious neighborhoods over time.

The method (1000) further includes receiving an input from the userrelated to a city of interest (1014), and retrieving a plurality offeature vectors associated with neighborhoods located in or adjacent tothe city of interest (1016). In an embodiment, a plurality of featurevectors located only in the city of interest may be retrieved. Inanother embodiment, if the city of interest is a part of a largermetropolitan area, neighborhoods located in the city of interest as wellas neighborhoods adjacent to the city of interest may be retrieved froma database. As an example, if the user's input indicates that the useris interested in moving to Washington, D.C., a plurality of featurevectors associated with neighborhoods in D.C. as well as withneighborhoods in Maryland or Virginia bordering D.C. may be retrieved. Aboundary of geographic areas in or adjacent to the city of interest foranalysis may be determined by several factors, such as the user'spreference for commute distance, the user's financial data, preferredfeatures of a neighborhood, or the like.

The method (1000) further includes comparing the combination timelinefeature vector with each of the plurality of feature vectors associatedwith the neighborhoods in or adjacent to the city of interest (1018).After comparison, the method also includes determining one or more ofthe neighborhoods in or adjacent to the city of interest characterizedby a minimum distance between the combination timeline feature vectorand one of the plurality of feature vectors for new neighborhoods(1020). A minimum distance between the combination timeline featurevector and a feature vector associated with a new neighborhood indicatesthat the new neighborhood has characteristics which most resemble thosepreferred characteristics of the user's previous neighborhoods.

In an embodiment of the invention, the method may further includeranking one or more of the neighborhoods in or adjacent to the city ofinterest by increasing distance between the combination feature vectorand the plurality of feature vectors associated with new neighborhoods.The ranking of the new neighborhoods may be displayed on a screen of theuser's computing device. In addition to displaying ranking, thedescription or reviews of each neighborhood by people who are familiarwith the neighborhood may be displayed next to the ranking. For example,if a neighborhood in the new city has neighborhood characteristicsdefined by the cloud reviews by the people who actually live in theneighborhood, the reviews may be provided as a link on a display screennext to the ranking so that the user can view subjective commentsregarding the neighborhood.

After reviewing the best matched neighborhoods in or adjacent to the newcity, the user may select a new neighborhood to obtain additionalinformation related to the new neighborhood. After receiving the userselection of a desired neighborhood, a neighborhood ranking system (200)in accordance with an embodiment of the present invention may compute adifference between at least one of the timeline feature vectors of theprevious neighborhoods and the feature vector associated with a newneighborhood selected by the user. The difference (or similarities)between the previous neighborhood and the new neighborhood may bedisplayed for the user to review. For example, the difference mayinclude a cost differential between a similar home in the selected newneighborhood and at least one of the user's previous neighborhoods. Theother differences such as school ranking, property tax rate,demographics, or the like, may also be displayed for the user's review.

After the user selects a neighborhood in or adjacent to the city ofinterest, the user may be presented with an option to view homesavailable for sale or for rent in the selected neighborhood. Inembodiments of the present invention, the search results of homes may becombined with the user's financial state (e.g., using the PFM software),and the user's affordability of each home may be calculated in view ofthe user's current or projected future financial state. The calculationmay include the total cost of home ownership, such as mortgage payment,home insurance and other hazard insurance, property tax, generalmaintenance cost, and the like.

It should be appreciated that the specific steps illustrated in FIG. 10provide a particular method of suggesting a new neighborhood accordingto an embodiment of the present invention. Other sequences of steps mayalso be performed according to alternative embodiments. For example,alternative embodiments of the present invention may perform the stepsoutlined above in a different order. Moreover, the individual stepsillustrated in FIG. 10 may include multiple sub-steps that may beperformed in various sequences as appropriate to the individual step.Furthermore, additional steps may be added or removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

FIG. 11 is a high level flowchart illustrating a method of suggesting anew neighborhood in a new geographic area based classification of theuser's current neighborhood relative to the current city according to anembodiment of the present invention. Typically, each city contains anumber of different neighborhoods or subdivisions located in or adjacentto the city, where each neighborhood has distinct characteristics. Inembodiments of the present invention, new neighborhoods in the new citywith the same or similar classification as the current neighborhood maybe presented to the user. As an example, if the user is moving from alow cost area to a high cost area and if the user is currently living ina neighborhood with a mean home value of about 10 percent above themedian, then the user may be presented with new neighborhoods rangingfrom 5 to 20 percent above the median for the new geographic area.

In embodiments of the present invention, one of the search criteria fora new neighborhood in or adjacent to a city of interest may include aclassification of one or more characteristics of a current neighborhoodrelative to the current city. As an example, if the user would like tofind a neighborhood with a mean home value which is about 50 percentover a mean home value of all the neighborhoods combined in the city,then such a criterion may be included during a feature vector analysis.As an illustration, let say a user currently lives in a city with a meanhome value of $500,000, whereas his immediate neighborhood within thecity has a mean home value of $750,000 (i.e., about 50% greater). Whenthe user searches for a neighborhood in a new city with a mean homevalue of about $200,000, the user may desire to live in a similarlyupscale new neighborhood with a mean home value of about $300,000 (i.e.,about 50% greater). While a mean home value is used as an illustration,any suitable characteristics (e.g., school ranking, distance to freeway,or the like) of a neighborhood relative to corresponding characteristicsof a new city may be used as one of the search criteria for a newneighborhood.

As shown in FIG. 11, the method (1100) includes computing a currentfeature vector using characteristics associated with a currentneighborhood within a current city (1110). As described above, a numberof different characteristics of a neighborhood (e.g., average homeprice, average home size, lot size, distance to school, school rank, orthe like) can be represented as an N-dimensional vector of numericalfeatures. The method also includes calculating a first percentdifference between a mean value of a selected characteristic associatedwith the current neighborhood and a mean value of a correspondingselected characteristic associated with the current city (1112). Inother words, the first percent difference represents a spread of aselected characteristic associated with the current neighborhoodcompared to the median. As described above, a selected characteristicmay be any characteristic associated with a neighborhood, such as a meanhome value, school ranking, distance to elementary schools, crime rate,or the like.

The method (1100) also includes receiving an input from the user relatedto city of interest (1114), and retrieving a plurality of featurevectors associated with neighborhoods in or adjacent to the city ofinterest (1116). The method further includes calculating a secondpercent difference between a mean value of the selected characteristicassociated with each of the neighborhoods in or adjacent to the city ofinterest and a mean value of the corresponding selected characteristicassociated with the city of interest (1118). The second percentdifference represents a spread of a selected characteristic associatedwith a new neighborhood compared to the median (e.g., a mean value of acorresponding characteristic of the new city).

The method (1100) further includes determining one or more neighborhoodsin or adjacent to the city of interest characterized by: (1) a minimumdifference between the current feature vector and each of the pluralityof feature vectors; and (2) the first percent difference being similarto the second percent difference (1120). A threshold for similaritybetween the first percent difference and the second percent differencecan be pre-set at a predetermined level (e.g., within 1, 5, 10, 20, 30percent, or the like). A minimum distance between the current featurevector and a feature vector associated with a new neighborhood indicatesthat the new neighborhood has characteristics which are most similar tothose of the user's current neighborhood. When the first percentdifference and the second percent difference are similar, it indicatesthat a new neighborhood with the same classification for a selectedcharacteristic (as the current neighborhood) will be presented to theuser.

In embodiments shown in FIG. 11, while a selected characteristic of aneighborhood relative to the corresponding characteristic of a city isexpressed as a separate component in the determining step, the percentdifference of the selected characteristic may be incorporated as a partof a feature vector for the neighborhood. In other words, theclassification of a selected characteristic such as a mean home value ofa neighborhood compared to the median may be represented as numericalfeature in a feature vector, along with school ranking, crime rate,distance to freeways, or the like.

It should be appreciated that the specific steps illustrated in FIG. 11provide a particular method of suggesting a new neighborhood accordingto an embodiment of the present invention. Other sequences of steps mayalso be performed according to alternative embodiments. For example,alternative embodiments of the present invention may perform the stepsoutlined above in a different order. Moreover, the individual stepsillustrated in FIG. 11 may include multiple sub-steps that may beperformed in various sequences as appropriate to the individual step.Furthermore, additional steps may be added or removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

The methods described herein can be implemented by a processor coupledto a non-transitory computer readable storage media as described morefully below.

FIG. 6 is high level schematic diagram illustrating a computer systemincluding instructions to perform any one or more of the methodologiesdescribed herein. A system 600 includes a computer 610 connected to anetwork 614. The computer 610 includes a processor 620 (also referred toas a data processor), a storage device 622, an output device 624, aninput device 626, and a network interface device 628, all connected viaa bus 630. The processor 620 represents a central processing unit of anytype of architecture, such as a CISC (Complex Instruction SetComputing), RISC (Reduced Instruction Set Computing), VLIW (Very LongInstruction Word), or a hybrid architecture, although any appropriateprocessor may be used. The processor 620 executes instructions andincludes that portion of the computer 610 that controls the operation ofthe entire computer. Although not depicted in FIG. 6, the processor 620typically includes a control unit that organizes data and programstorage in memory and transfers data and other information between thevarious parts of the computer 610. The processor 620 receives input datafrom the input device 626 and the network 614 reads and stores code anddata in the storage device 622 and presents data to the output device624.

Although the computer 610 is shown to contain only a single processor620 and a single bus 630, the disclosed embodiment applies equally tocomputers that may have multiple processors and to computers that mayhave multiple busses with some or all performing different functions indifferent ways.

The storage device 622 represents one or more mechanisms for storingdata. For example, the storage device 622 may include read-only memory(ROM), random access memory (RAM), magnetic disk storage media, opticalstorage media, flash memory devices, and/or other machine-readablemedia. In other embodiments, any appropriate type of storage device maybe used. Although only one storage device 622 is shown, multiple storagedevices and multiple types of storage devices may be present. Further,although the computer 610 is drawn to contain the storage device 622, itmay be distributed across other computers, for example on a server.

The storage device 622 includes a controller (not shown in FIG. 6) anddata items 634. The controller includes instructions capable of beingexecuted on the processor 620 to carry out the methods described morefully throughout the present specification. In another embodiment, someor all of the functions are carried out via hardware in lieu of aprocessor-based system. In one embodiment, the controller is a webbrowser, but in other embodiments the controller may be a databasesystem, a file system, an electronic mail system, a media manager, animage manager, or may include any other functions capable of accessingdata items. Of course, the storage device 622 may also containadditional software and data (not shown), which is not necessary tounderstand the invention.

Although the controller and the data items 634 are shown to be withinthe storage device 622 in the computer 610, some or all of them may bedistributed across other systems, for example on a server and accessedvia the network 614.

The output device 624 is that part of the computer 610 that displaysoutput to the user. The output device 624 may be a liquid crystaldisplay (LCD) well-known in the art of computer hardware. But, in otherembodiments the output device 624 may be replaced with a gas orplasma-based flat-panel display or a traditional cathode-ray tube (CRT)display. In still other embodiments, any appropriate display device maybe used. Although only one output device 624 is shown, in otherembodiments any number of output devices of different types, or of thesame type, may be present. In an embodiment, the output device 624displays a user interface.

The input device 626 may be a keyboard, mouse or other pointing device,trackball, touchpad, touch screen, keypad, microphone, voice recognitiondevice, or any other appropriate mechanism for the user to input data tothe computer 610 and manipulate the user interface previously discussed.Although only one input device 626 is shown, in another embodiment anynumber and type of input devices may be present.

The network interface device 628 provides connectivity from the computer610 to the network 614 through any suitable communications protocol. Thenetwork interface device 628 sends and receives data items from thenetwork 614.

The bus 630 may represent one or more busses, e.g., USB (UniversalSerial Bus), PCI, ISA (Industry Standard Architecture), X-Bus, EISA(Extended Industry Standard Architecture), or any other appropriate busand/or bridge (also called a bus controller).

The computer 610 may be implemented using any suitable hardware and/orsoftware, such as a personal computer or other electronic computingdevice. Portable computers, laptop or notebook computers, PDAs (PersonalDigital Assistants), pocket computers, appliances, telephones, andmainframe computers are examples of other possible configurations of thecomputer 610. For example, other peripheral devices such as audioadapters or chip programming devices, such as EPROM (ErasableProgrammable Read-Only Memory) programming devices may be used inaddition to, or in place of, the hardware already depicted.

The network 614 may be any suitable network and may support anyappropriate protocol suitable for communication to the computer 610. Inan embodiment, the network 614 may support wireless communications. Inanother embodiment, the network 614 may support hard-wiredcommunications, such as a telephone line or cable. In anotherembodiment, the network 614 may support the Ethernet IEEE (Institute ofElectrical and Electronics Engineers) 802.3x specification. In anotherembodiment, the network 614 may be the Internet and may support IP(Internet Protocol). In another embodiment, the network 614 may be alocal area network (LAN) or a wide area network (WAN). In anotherembodiment, the network 614 may be a hotspot service provider network.In another embodiment, the network 614 may be an intranet. In anotherembodiment, the network 614 may be a GPRS (General Packet Radio Service)network. In another embodiment, the network 614 may be any appropriatecellular data network or cell-based radio network technology. In anotherembodiment, the network 614 may be an IEEE 802.11 wireless network. Instill another embodiment, the network 614 may be any suitable network orcombination of networks. Although one network 614 is shown, in otherembodiments any number of networks (of the same or different types) maybe present.

A user computer 650 can interact with computer 610 through network 614.The user computer 650 includes a processor 652, a storage device 654,and an input/output device 656. The description related to processor 620and storage device 622 is applicable to processor 652 and storage device654. As an example, the user computer 650 can be a personal computer,laptop computer, or the like, operated by a member of a membershiporganization (e.g., an insurance company, a financial institution, areal estate company, or the like). Using the user computer 650, themember can then interact with computer 610 operated by such anorganization, such as the present assignee, through network 614 in orderto access the organization web pages or the like.

The embodiments described herein may be implemented in an operatingenvironment comprising software installed on any programmable device, inhardware, or in a combination of software and hardware. Althoughembodiments have been described with reference to specific exampleembodiments, it will be evident that various modifications and changesmay be made to these embodiments without departing from the broaderspirit and scope of the invention. Accordingly, the specification anddrawings are to be regarded in an illustrative rather than a restrictivesense.

It is also understood that the examples and embodiments described hereinare for illustrative purposes only and that various modifications orchanges in light thereof will be suggested to persons skilled in the artand are to be included within the spirit and purview of this applicationand scope of the appended claims.

What is claimed is:
 1. A method of determining a geographic area havingsimilar characteristics to a first geographic area associated with auser, the method comprising: providing a web-based system with agraphical user interface on a computer to receive at least one inputrelated to a location of interest; upon receiving the at least one inputon the graphical user interface, assigning weights to user preferencesbased on user-provided information retrieved from one or more socialmedia platforms and based on the at least one input; receiving featuredata related to the location of interest, the feature data including Nfeatures associated with the location of interest; transforming the Nfeatures into N feature values associated with the location of interest;generating an M-dimensional feature vector representative of at leastone other geographic area associated with the location of interest, theM-dimensional vector comprising the N feature values and the at leastone input, the N feature values and the at least one input scaled basedon the weights; identifying with the web-based system the at least oneother geographic area associated with the location of interest;analyzing the M-dimensional vector with reference to a current featurevector to determine a difference between the M-dimensional vector andthe current feature vector, the current feature vector associated withthe first geographic area; displaying the difference between theM-dimensional vector and the current vector on the graphical userinterface; and receiving via the web-based system a supplemental inputand adjusting at least one of the feature vector or the current vectorbased on the supplemental input, wherein at least one of thecharacteristics from the first geographic area is not based on imageanalysis of the first geographic area.
 2. The method of claim 1 whereinthe at least one other geographic area is defined by a non-contiguousarea.
 3. The method of claim 1 wherein a boundary of the firstgeographic area is drawn freehand by a user through a graphical userinterface.
 4. The method of claim 1, the first geographic areaassociated with the user is a current neighborhood of the user.
 5. Themethod of claim 1, the first geographic area associated with the user isa neighborhood indicated by the user to appeal to the user.
 6. Themethod of claim 1, the difference is calculated by determining aEuclidean distance between the M-dimensional vector and the currentvector.
 7. A system for determining a geographic area having soughtcharacteristics, the system comprising: a processor; and acomputer-readable medium storing a plurality of instructions that, whenexecuted, cause the processor to effectuate operations comprising:providing a web-based system with a graphical user interface to receiveat least one input related to a city of interest; upon receiving the atleast one input on the graphical user interface, assigning weights touser preferences based on user-provided information retrieved from oneor more social media platforms and based on the at least one input;identifying with the web-based system at least one other geographic areaassociated with the city of interest; receiving feature data related toeach of the at least one other geographic area associated with the cityof interest, the feature data including N features associated with eachof the at least one other geographic area; transforming the N featuresinto N feature values associated with each of the at least one othergeographic area; generating at least one M-dimensional feature vectorrepresentative of the at least one other geographic area associated withthe city of interest, the at least one M-dimensional vector comprisingthe N feature values and the at least one input, the N feature valuesand the at least one input scaled based on the weights; analyzing the atleast one M-dimensional vector with reference to a current featurevector to determine at least one difference between the at least oneM-dimensional vector and the current feature vector; displaying the atleast one difference between the at least one M-dimensional vector andthe current vector on the graphical user interface; and receiving viathe web-based system a supplemental input and adjusting at least one ofthe feature vector or the current vector based on the supplementalinput, wherein at least one of the sought characteristics is not basedon image analysis of the first geographic area.
 8. The system of claim7, wherein sought characteristics are associated with a first geographicarea, and the first geographic area and the at least one othergeographic area are associated with neighborhoods.
 9. The system ofclaim 7, further comprising: generating a bell curve associated with aplurality of geographic areas, the bell curve having at least Ndimensions, the bell curve illustrating the difference such that minimumdifference is represented at a peak of the bell curve, wherein the atleast one other geographic area includes the plurality of geographicareas.
 10. The system of claim 7 wherein sought characteristics areassociated with a first geographic area, and a boundary of the firstgeographic area is defined by a dissimilarity threshold in reference tothe sought characteristics.
 11. The system of claim 7, wherein thecurrent vector is a baseline feature vector representing a baseline of ageographic area.
 12. A non-transitory computer-readable storage mediumcomprising a plurality of computer readable instructions tangiblyembodied on the computer-readable storage medium that, when executed bya processor, cause the processor to effectuate operations comprising:providing a web-based system with a graphical user interface on acomputer to receive at least one input related to a city of interest;upon receiving the at least one input on the graphical user interface,assigning weights to user preferences based on user-provided informationretrieved from one or more social media platforms and based on the atleast one input; receiving feature data related to one or moregeographic areas of the city of interest, the feature data including Nfeatures associated with each of the one or more geographic areas;transforming the N features into N feature values associated with eachof the one or more geographic areas; generating one or moreM-dimensional feature vectors representative of the one or moregeographic areas associated with the city of interest, the one or moreM-dimensional vectors comprising the N feature values and the at leastone input, the N feature values and the at least one input scaled basedon the weights; analyzing the one or more M-dimensional vectors withreference to a current feature vector to determine one or moredifferences between the one or more M-dimensional vectors and thecurrent feature vector; and displaying one or more difference valuesbetween the one or more M-dimensional vectors and the current vector onthe graphical user interface; and receiving via the web-based system asupplemental input and adjusting at least one of the feature vector orthe current vector based on the supplemental input, wherein at least oneof the features from the one or more geographic areas is not based onimage analysis.
 13. The non-transitory computer-readable storage mediumof claim 12 wherein the at least one input is a free text input, thefree text input is mapped for use in generating the one or moreM-dimensional vectors.
 14. The non-transitory computer-readable storagemedium of claim 12 wherein a boundary of a first geographic area isdefined by a user through the graphical user interface, the firstgeographic area associated with a location of the user.